1. Field of the Invention
The present invention relates to method and apparatus for target detection and acquisition. More specifically, the present invention relates to the detection and acquisition of small targets in extremely cluttered background environments.
While the present invention is described herein with reference to illustrative embodiments for particular applications, it should be understood that the invention is not limited thereto. Those having ordinary skill in the art and access to the teachings provided herein will recognize additional modifications, applications, and embodiments within the scope thereof and additional fields in which the present invention would be of significant utility.
2. Description of the Related Art
The detection and acquisition of small targets in cluttered backgrounds has heretofore been somewhat problematic. For this purpose, a cluttered background is generally regarded as one in which there are many objects in an image having comparable size and intensity. Several techniques are known in the art, none of which are fully effective to suppress bright clutter objects in a cluttered background image.
Spatial filtering, for example, is a static technique which involves object discrimination on the basis of the dimensional criteria or size. However, when the background includes many objects of dimensions close to that of the target, spatial filtering is inadequate for many currently more demanding applications.
Hence, several techniques have been developed in which target acquisition and detection is effected based on the motion of the target relative to the background. One such technique is disclosed in U.S. Pat. No. 4,937,878, entitled SIGNAL PROCESSING FOR AUTONOMOUS ACQUISITION OF OBJECTS IN CLUTTERED BACKGROUND, issued Jun. 26, 1990 to Lo et al. This reference discloses an image processing acquisition system in which an image of the object is generated by first generating a difference between a current image frame and an image frame taken at a previous time, and second, generating a difference between the current image frame and another previous image frame (even earlier than the first image frame). This subtraction procedure eliminates some of the background clutter since the background scenes of the three image frames are essentially the same. The two difference image frames are then logically ANDed such that only the current position of the object of interest is detected.
A second motion based method of detecting a moving object within a cluttered background is disclosed in U.S. patent application No. 5,109,435, issued Apr. 28, 1992, by Sacks et al., entitled SEGMENTATION METHOD FOR USE AGAINST MOVING OBJECTS. In that application, the image frames, including the object of interest, of three consecutive images are correlated together. A median value for each pixel position of the correlated image frames is then selected, and each median pixel value is subtracted from the pixel value of one of the image frames to form a difference image. A threshold intensity value is determined for each pixel position. The threshold values are associated with the object of interest.
Although these two systems have met with favorable success, a need remained for further improvements in the art. U.S. patent application No. 5,150,426, issued Sep. 22, 1992, by Banh et al., entitled MOVING TARGET DETECTION METHOD USING TWO-FRAME SUBTRACTION, (the teachings of which are incorporated herein by reference) discloses a technique which effects the detection of moving objects within a scene by use of a single subtraction of two registered frames of video data. The ability to separate the object of interest from the background clutter by a single subtraction of two registered frames is realized by the combination of scene subtraction, filtering by a minimum difference processor (MDP) filter and multiplying the filtered difference image with the filtered live image.
Though somewhat effective in eliminating background clutter leakage, a need for further improvement remains due to inaccurate registration and dead cells in image sensors by way of example. With respect to the registration problem, it has been found that the performance of the referenced system in terms of clutter cancellation is coupled to image registration accuracy. Extreme registration accuracy is difficult to achieve. Accordingly, this limitation has heretofore been somewhat persistent.
A dead cell is an element of the image sensor that behaves differently from the average. Thus, as the camera moves, the locus of the cell in the object plane moves preventing the subtraction of background data in the two image frames. Hence, dead cells and other camera artifacts (e.g., gain imbalance) adversely affect background clutter leakage.
Accordingly, a need remains in the art for further improvements in conventional frame subtraction MTI processes affording lower false alarm rates and higher detection probabilities.